Unmanned ground vehicles (UGVs) include remote-driven or self-driven land vehicles that can carry cameras, sensors, communications equipment, or other payloads. Self-driven or “autonomous” land vehicles are essentially robotic platforms that are capable of operating outdoors and over a wide variety of terrain.
Autonomous land vehicles can travel at various speeds under diverse road constructs. For example, an autonomous land vehicle can travel at the speed limit when traffic is sparse, at low speed during a traffic jam, or can stop at a traffic light. The autonomous land vehicle can also travel at a constant speed, as well as accelerate or decelerate. The road on which the vehicle traverses can be straight, curved, uphill, downhill, or have many undulations. The number of lanes on the road can vary, and there are numerous types of road side constructs such as curbs, lawns, ditches, or pavement. Objects on and off the road such as cars, cycles, and pedestrians add more complexity to the scenario. It is important to accurately classify these road elements in order that the vehicle can navigate safely.
In one navigation system for autonomous land vehicles, a laser detection and ranging (LADAR) sensor is used to measure the range to each point within a scan that sweeps across a horizontal line. On-board global positioning system (GPS) and inertial navigation system (INS) sensors provide the geo-location and dynamics of the vehicle, which includes the position and altitude of the vehicle in world coordinates, as well as the velocity and angular velocity of the vehicle. This navigation system for autonomous land vehicles often has difficulty in processing the LADAR data and combining the GPS/INS data to accurately classify each range measurement in a scan into one of traversable, non-traversable, lane-mark, and obstacle classes. Classification of the range measurements based only on one input scan and its corresponding GPS/INS input is not robust enough with the diversity of vehicle states and road configurations that can be encountered.
An alternate navigation system classifies each range measurement in a scan based on the history of recent range scans. A fixed-size history buffer is employed having a size based on a fixed number of range scans. Consequently, the distance covered by the range scans saved in this buffer depends on the speed of the vehicle. When the vehicle travels at high speed, the area coverage in a fixed number of scans is large. When the vehicle travels at slow speed, the area coverage is small. Using the scans in the fixed-size buffer for ground plane estimation causes varying degrees of inaccuracy.